We study the problem of hierarchical classification when labels corresponding to partial and/or multiple paths in the underlying taxonomy are allowed. We introduce a new hierarchi...
Reinforcement learning (RL) can be impractical for many high dimensional problems because of the computational cost of doing stochastic search in large state spaces. We propose a ...
This paper explores the increasing the heterogeneity of an agent population to stabilize decentralized systems by adding bias terms to each agent's expected payoffs. Two appr...
Abstract. We present a scalable and flexible grouping service based on concast and best-effort single-source multicast. The service assigns participating end systems to specific gr...
— This paper quantifies the information rate of multiple-input multiple-output (MIMO) systems with finite rate channel state feedback and power on/off strategy. In power on/off...
Wei Dai, Youjian Liu, Brian Rider, Vincent K. N. L...